Shopping checkout (e.g., retail, supermarket, etc.) is a process by which most everyone is familiar. Typical checkout involves a shopper navigating about a store collecting items for purchase. Upon completion of gathering the desired items, the shopper proceeds to a point-of sale (POS) checkout station for checkout (e.g., bagging and payment). POS systems are used in supermarkets, restaurants, hotels, stadiums, casinos, as well as almost any type of retail establishment, and typically include three separate functions that today are mostly lumped together at a single POS station: (1) enumerating each item to be purchased, and determining its price (typically, by presenting it to a bar code scanner), (2) verifying that each item is what it was claimed to be, and (3) paying for the item(s).
Unfortunately, with increased volumes of shoppers and instances of operator collusion, theft is growing at an alarming rate. In an attempt to detect operator collusion, the bodily movements of cashiers are monitored and analyzed to determine whether the movements are typical. Atypical movements by a cashier may indicate that a security breach has occurred. In the process of detecting cashiers' behavior, visual attribute information from an image is used to detect the position of the hands and/or arms of the cashier. However, to make a model for cashiers' hands and arms using prior art approaches, it is necessary to manually input the skin/attire color of each cashier, or use a generic public database. In the case of the former, the work of inputting the skin color of each cashier is a burden. In the case of the latter it is difficult to get high performance using a public database due to the variation of skin color, attire color, and light conditions.